Delineation of individual tree crowns for mobile laser scanning data
Bibliographic record
Abstract
The information of individual trees plays an important role in urban surveying and mapping. With the development of Light Detection and Ranging (LiDAR) technology, 3-Dimenisonal (3D) structure of trees can be generated in point clouds with high spatial resolution and accuracy. Individual tree segmentations are used to derive tree structural attributes such as tree height, crown diameter, stem position etc. In this study, a framework is proposed to take advantage of the detailed structures of tree crowns which are represented in the mobile laser scanning (MLS) data. This framework consists of five steps: (1) Automatically detect and remove ground points using RANSAC; (2) Compress all the above ground points to image grid with 3D knowledge reserved; (3) Simplify and remove unqualified grids; (4) Find tree peaks using a heuristic searching method; (5) Delineate the individual tree crowns by applying a modified watershed method. In an experiment on the point clouds on Xiamen Island, China, individual tree crowns from MLS point cloud data are successfully extracted.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".